2015

An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.

2002

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.

In this paper we investigate connections between statistical learning
theory and data compression on the basis of support vector machine
(SVM) model selection. Inspired by several generalization bounds we
construct ``compression coefficients'' for SVMs, which measure the
amount by which the training labels can be compressed by some
classification hypothesis. The main idea is to relate the coding
precision of this hypothesis to the width of the margin of the
SVM. The compression coefficients connect well known quantities such
as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel
matrix and the number of support vectors. To test whether they are
useful in practice we ran model selection experiments on several real
world datasets. As a result we found that compression coefficients can
fairly accurately predict the parameters for which the test error is
minimized.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems